235 lines
9.0 KiB
Python
235 lines
9.0 KiB
Python
import argparse
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import time
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from torch.utils.data import DataLoader
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import test # Import test.py to get mAP after each epoch
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from models import *
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from utils.datasets import *
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from utils.utils import *
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import torch.distributed as dist
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def train(
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cfg,
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data_cfg,
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img_size=416,
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resume=False,
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epochs=270,
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batch_size=16,
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accumulate=1,
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multi_scale=False,
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freeze_backbone=False,
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num_workers=0
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):
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weights = 'weights' + os.sep
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latest = weights + 'latest.pt'
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best = weights + 'best.pt'
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device = torch_utils.select_device()
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if multi_scale:
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img_size = 608 # initiate with maximum multi_scale size
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else:
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torch.backends.cudnn.benchmark = True # unsuitable for multiscale
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# Configure run
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train_path = parse_data_cfg(data_cfg)['train']
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# Initialize model
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model = Darknet(cfg, img_size).to(device)
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# Optimizer
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lr0 = 0.001 # initial learning rate
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optimizer = torch.optim.SGD(model.parameters(), lr=lr0, momentum=.9,weight_decay = 0.0005)
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cutoff = -1 # backbone reaches to cutoff layer
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start_epoch = 0
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best_loss = float('inf')
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if resume: # Load previously saved PyTorch model
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checkpoint = torch.load(latest, map_location=device) # load checkpoint
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model.load_state_dict(checkpoint['model'])
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start_epoch = checkpoint['epoch'] + 1
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if checkpoint['optimizer'] is not None:
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optimizer.load_state_dict(checkpoint['optimizer'])
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best_loss = checkpoint['best_loss']
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del checkpoint # current, saved
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else: # Initialize model with backbone (optional)
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if cfg.endswith('yolov3.cfg'):
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cutoff = load_darknet_weights(model, weights + 'darknet53.conv.74')
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elif cfg.endswith('yolov3-tiny.cfg'):
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cutoff = load_darknet_weights(model, weights + 'yolov3-tiny.conv.15')
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#initialize for distributed training
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if torch.cuda.device_count() > 1:
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dist.init_process_group(backend=opt.dist_backend, init_method=opt.dist_url,world_size=opt.world_size, rank=opt.rank)
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model = torch.nn.parallel.DistributedDataParallel(model)
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# Dataloader
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dataset = LoadImagesAndLabels(train_path, img_size=img_size, augment=True)
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if torch.cuda.device_count() > 1:
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train_sampler = torch.utils.data.distributed.DistributedSampler(dataset)
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else:
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train_sampler=None
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dataloader = DataLoader(dataset, batch_size=batch_size, num_workers=num_workers,sampler=train_sampler)
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# Transfer learning (train only YOLO layers)
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# for i, (name, p) in enumerate(model.named_parameters()):
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# p.requires_grad = True if (p.shape[0] == 255) else False
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# Set scheduler
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# scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=[54, 61], gamma=0.1)
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# Start training
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t0 = time.time()
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model_info(model)
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n_burnin = min(round(len(dataloader) / 5 + 1), 1000) # burn-in batches
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for epoch in range(epochs):
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model.train()
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epoch += start_epoch
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print(('\n%8s%12s' + '%10s' * 7) % (
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'Epoch', 'Batch', 'xy', 'wh', 'conf', 'cls', 'total', 'nTargets', 'time'))
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# Update scheduler (automatic)
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# scheduler.step()
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# Update scheduler (manual)
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lr = lr0 / 10 if epoch > 250 else lr0
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for x in optimizer.param_groups:
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x['lr'] = lr
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# Freeze backbone at epoch 0, unfreeze at epoch 1
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if freeze_backbone and epoch < 2:
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for i, (name, p) in enumerate(model.named_parameters()):
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if int(name.split('.')[1]) < cutoff: # if layer < 75
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p.requires_grad = False if (epoch == 0) else True
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rloss = defaultdict(float)
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for i, (imgs, targets, _, _) in enumerate(dataloader):
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# Unpad and collate targets
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for j, t in enumerate(targets):
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t[:, 0] = j
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targets = torch.cat([t[t[:, 5].nonzero()] for t in targets], 0).squeeze(1)
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nT = len(targets)
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if nT == 0: # if no targets continue
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continue
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# Plot images with bounding boxes
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plot_images = False
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if plot_images:
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import matplotlib.pyplot as plt
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plt.figure(figsize=(10, 10))
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for ip in range(batch_size):
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labels = xywh2xyxy(targets[targets[:, 0] == ip, 2:6]).numpy() * img_size
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plt.subplot(4, 4, ip + 1).imshow(imgs[ip].numpy().transpose(1, 2, 0))
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plt.plot(labels[:, [0, 2, 2, 0, 0]].T, labels[:, [1, 1, 3, 3, 1]].T, '.-')
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plt.axis('off')
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# SGD burn-in
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if (epoch == 0) and (i <= n_burnin):
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lr = lr0 * (i / n_burnin) ** 4
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for x in optimizer.param_groups:
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x['lr'] = lr
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# Run model
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pred = model(imgs.to(device))
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# Build targets
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target_list = build_targets(model, targets.to(device), pred)
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# Compute loss
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loss, loss_dict = compute_loss(pred, target_list)
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# Compute gradient
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loss.backward()
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# Accumulate gradient for x batches before optimizing
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if (i + 1) % accumulate == 0 or (i + 1) == len(dataloader):
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optimizer.step()
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optimizer.zero_grad()
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# Running epoch-means of tracked metrics
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for key, val in loss_dict.items():
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rloss[key] = (rloss[key] * i + val) / (i + 1)
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s = ('%8s%12s' + '%10.3g' * 7) % (
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'%g/%g' % (epoch, epochs - 1),
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'%g/%g' % (i, len(dataloader) - 1),
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rloss['xy'], rloss['wh'], rloss['conf'],
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rloss['cls'], rloss['total'],
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nT, time.time() - t0)
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t0 = time.time()
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print(s)
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# Multi-Scale training (320 - 608 pixels) every 10 batches
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if multi_scale and (i + 1) % 10 == 0:
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dataset.img_size = random.choice(range(10, 20)) * 32
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print('multi_scale img_size = %g' % dataset.img_size)
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# Update best loss
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if rloss['total'] < best_loss:
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best_loss = rloss['total']
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# Save training results
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save = True
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if save:
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# Save latest checkpoint
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checkpoint = {'epoch': epoch,
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'best_loss': best_loss,
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'model': model.module.state_dict() if type(model) is nn.parallel.DistributedDataParallel else model.state_dict(),
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'optimizer': optimizer.state_dict()}
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torch.save(checkpoint, latest)
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# Save best checkpoint
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if best_loss == rloss['total']:
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os.system('cp ' + latest + ' ' + best)
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# Save backup weights every 5 epochs (optional)
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if (epoch > 0) and (epoch % 5 == 0):
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os.system('cp ' + latest + ' ' + weights + 'backup{}.pt'.format(epoch))
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# Calculate mAP
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if type(model) is nn.parallel.DistributedDataParallel:
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model = model.module
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with torch.no_grad():
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P, R, mAP = test.test(cfg, data_cfg, weights=latest, batch_size=batch_size, img_size=img_size, model=model)
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# Write epoch results
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with open('results.txt', 'a') as file:
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file.write(s + '%11.3g' * 3 % (P, R, mAP) + '\n')
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument('--epochs', type=int, default=270, help='number of epochs')
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parser.add_argument('--batch-size', type=int, default=16, help='size of each image batch')
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parser.add_argument('--accumulate', type=int, default=1, help='accumulate gradient x batches before optimizing')
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parser.add_argument('--cfg', type=str, default='cfg/yolov3.cfg', help='cfg file path')
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parser.add_argument('--data-cfg', type=str, default='cfg/coco.data', help='coco.data file path')
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parser.add_argument('--multi-scale', action='store_true', help='random image sizes per batch 320 - 608')
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parser.add_argument('--img-size', type=int, default=32 * 13, help='pixels')
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parser.add_argument('--resume', action='store_true', help='resume training flag')
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parser.add_argument('--num-workers', type=int, default=4, help='number of Pytorch DataLoader workers')
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parser.add_argument('--dist-url', default='tcp://127.0.0.1:9999', type=str,help='url used to set up distributed training')
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parser.add_argument('--rank', default=0, type=int,help='node rank for distributed training')
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parser.add_argument('--world-size', default=1, type=int,help='number of nodes for distributed training')
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parser.add_argument('--dist-backend', default='nccl', type=str,help='distributed backend')
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opt = parser.parse_args()
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print(opt, end='\n\n')
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init_seeds()
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train(
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opt.cfg,
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opt.data_cfg,
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img_size=opt.img_size,
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resume=opt.resume,
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epochs=opt.epochs,
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batch_size=opt.batch_size,
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accumulate=opt.accumulate,
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multi_scale=opt.multi_scale,
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num_workers=opt.num_workers
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)
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